AI-Driven Predictive Resilience: Integrating Impact Forecasting, Governance, and Proactive Mitigation in Networks

Abstract

Interconnected networks such as supply chains, energy systems, transportation, telecommunications, digital platforms, and healthcare increasingly face disruptions from supply shortages, cyber threats, and sudden demand surges. This study proposes an integrated AI-driven resilience framework designed to help such networks anticipate, withstand, and recover from disruptions. Using publicly available operational time-series data, logistics records, and global disruption datasets, we develop a predictive pipeline that combines impact forecasting, ethical governance assessment, and proactive mitigation strategies. A Long Short-Term Memory (LSTM) model was used to forecast disruption patterns and showed strong predictive accuracy, outperforming traditional ARIMA approaches on normalized time-series data (RMSE = 0.0916; MAPE = 4.2%). To ensure responsible deployment, governance mechanisms were evaluated with AI Fairness 360, revealing notable bias risks that require further refinement. Model transparency was improved using SHAP explanations, which aligned well with operational expectations and supported interpretable decision-making. To test real-world impact, agent-based simulations with 1,000 agents evaluated how predictive insights improve network recovery. Results show significant resilience gains, including faster recovery, higher recovery rates, and about 23% cost savings compared with baseline scenarios. Overall, the framework demonstrates that scalable, desk-based AI methods can strengthen resilience across complex networks while supporting transparent and responsible decision-making for future infrastructure planning.